Annotated Bibliography
Write for each a summary that expresses some of the main points, and explores how they connect to your paper topic. This is like writing an annotated bibliography that could be part of the background for your imagined paper. The annotated bibliography should include summary of the text, reflection, criticism, analysis, etc. Each of the 3 entries should be between 300 and 350 words.
Artificial Knowing Gender and the Thinking Machine by Alison Adam 2 AI IN CONTEXT Marvin Minsky (1968: v) defines AI in these terms: ‘Artificial Intelligence is the science of making machines do things that would require intelligence if done by men.’ However, in describing what AI is about, I doubt whether it is ultimately useful to offer an immutable definition, and certainly not one which defines it in terms of men’s intelligence! The ‘artificial mind’ myth has contributed to a view of AI which, as well as being too mystical, may well be some distance from the intentions of those working in the field. This is especially problematic if, as I have suggested, AI researchers tend to view their discipline in terms of engineering, of designing and building computing artefacts; this does not tie in neatly with an ‘artificial mind’ view. It is hard to know why such a position has proved so per- sistent. It may be that for many, at least outside the confines of computing, their introduction to the subject comes from one of the widely known philosophical critiques, such as that of Dreyfus (1979; 1992) or Searle (1987). In such a case it would be easy to fix on the idea that the aim of AI is primarily to create an artificial mind, and that the success or failure of the whole AI project should be judged against this one goal. GENERAL PROBLEM-SOLVING - THE EARLY DAYS OF AI A researcher entering the field of AI at the end of the twentieth century enters a mature discipline with clear boundaries and a set of problems which are deemed to be appropriate for the subject, what Thomas Kuhn (1970) would have termed a ‘paradigm’, or Imre Lakatos (1970), a ‘research programme’. Forty or more years earlier, in an entirely new subject area, the choice of appropriate problem was not so clear. Essentially the defi- nition of what constituted an appropriate problem for AI was still open in the mid-195Os, when there were just a few key players, including Herbert Simon at Carnegie Technical Institute (later Carnegie Mellon University) collaborating with Allen Newell of the RAND Corporation, and Marvin Minsky collaborating with John McCarthy at the Massachusetts Institute of Technology (MIT), in efforts to produce a working computer system 34 AI IN CONTEXT which would set the standard for the nascent discipline. I argue that the kind of problems which were chosen in that period was remarkably sig- nificant, as it set the style for the symbolic AI programme which was to dominate AI research for the next three decades or so. In deciding what constituted appropriate intelligent, behaviour to be modelled in their com- puter systems, the new AI researchers naturally looked to themselves. As Tom Athanasiou (1985: 13, quoting the researcher Bob Wilensky, explains, They were interested in intelligence, and they needed somewhere to start. So they looked around at who the smartest people were, and they were themselves, of course. They were all essentially mathema- ticians by training, and mathematicians do two things - they prove theorems and play chess. And they said, hey, if it proves a theorem or plays chess, it must be smart. Although it was not the very first AI program to be developed, in the official history of the subject, the first significant AI program: is widely taken to be Allen Newell, J. C. Shaw and Herbert Simon’s (1963) Logic Theorist. Simon initially considered three tasks for the program: chess, geometry and logic theorem proving - the latter for no deeper reason, apparently, than he happened to have the two volumes of Bertrand Russell and Alfred North Whitehead’s Principia, the ‘bible’ of predicate logic, at home. In the history of AI, Logic Theorist is highly significant as it mapped out the field for AI search strategies and the use of heuristics which were developed from Simon’s own work on decision theory. In this, it can be seen how the ideal of rational decision-making was carried over into the concept of search in AI. Simon (1976: 20) characterizes rational decision-making as a process of listing alternative strategies, determining the consequences of each and then comparatively evaluating each consequence in turn. Decision theory applies sophisticated mathematics to decisions in a number of areas based on these precepts. Similarly, the idea of searching for a solution to an AI problem involves characterizing the problem as a number of discrete and formally described states, one or more of which will be a starting state of the problem and one or more of which will be a goal or solution state. Operations or rules, which move the problem from one state to another, and a test or evaluative function, which determines whether the problem has reached its goal or solution state, must also be defined. The problem then is seen in terms of a search for a solution, going from one state to another and another and so on until the goal is reached. Hence, the problem is moved from one formally defined state to another in some way which is regarded as rational, perhaps guided by a heuristic or rule of thumb which may help to find a solution more quickly.’ As Simon himself realized, the idealized model of decision-making was rarely, if ever, achievable in a real situation, since an individual could never 35 AI IN CONTEXT know all the alternatives and their consequences. But Simon’s critique was not an objection to the rationalistic approach as such, instead it was an objection to the assumption of full knowledge. Applying this idea to computer evaluations of decisions, the computer must operate within a bounded rationality. These ideas developed into more general theories of problem solving which I argue can be seen more particularly in the widely accepted idea of the search procedure in AI. It should not necessarily be taken for granted that solutions to problems are things to be searched for. Generally speaking, the idea of search, which is such a fundamental part of symbolic AI, is based on the ideal Cartesian method of deduction. This disguises the need to look at how other forms of problem solving based on intuition (a less prestigious form of reasoning) could be represented, where a search is not ostensibly part of the process. In addition, the emphasis of early AI on search was based on a model of rational, one step at a time, or serial, decision-making, in the same Cartesian mould. Such a process is excessively deterministic, and even by the admis- sion of its originator, impossible to achieve. Chapter three describes the limitations of this model as an ideal as it was based on limited empirical research on individuals making decisions in constrained circumstances. In the same vein, planning may be thought of as an adjunct to searching as it involves making the plan of which states to search, while searching involves actually carrying out the plan. Later empirical research on decision-making shows the extent to which individuals neither search for solutions nor plan their actions in a path towards a solution. Suchman (1987) shows the way that individuals react contingently to the situations in which they find themselves, in interactions with intelligent machines. They do not plan a serial, rational, step-by-step path to their goal, rather, they marshal a range of resources in order to deal with a variety of often unexpected settings. In other words computing systems built on a planning model tend to confuse people’s plans with their situated actions. Plans neither act as an adequate reconstruction of situated action nor do they determine its course. In the mid-19% programming a computer was no mean feat. Indeed such was the difficulty that Newell and Simon were led to ‘hand-simulate’ their program before implementing it on a computer. This they did by giving each of Simon’s wife and children a sub-routine to ‘execute’ when called upon to do so, an experience which his children apparently never forgot (Crevier 1993). We may be struck by the irony of having the bodily immanence of one’s children simulate the ideal of Cartesian reason in this way; certainly Simon was lucky to have a big enough and willing enough family to execute all his sub-routines. Human computers apart, the point I wish to make is not that there was a deliberate choice to start up the field of AI with an example which clearly venerated male reason over female reason; that, for example, pure mathematics was consciously chosen 36 AI IN CONTEXT instead of knowledge of child rearing or whatever. Rather, I argue that this kind of problem was the natural choice of workers in the field; an example of what is taken to be the highest form of reasoning, something that people find highly abstract and difficult, a masculine standard drawn from their own lives, which was then to form the subject matter of the first significant AI program. Newell’s and Simon’s (1963) later development of GPS (General Problem Solver) was produced as an attempt to mimic contextless general problem solving abilities, in the form of ‘means-ends analysis’, which was derived from subjects’ think-aloud protocols in solving logic problems, in a series of psychological experiments. The idea is that human subjects will select the most appropriate means to satisfy a given end, gradually reducing the difference between the start and the solution to the given problem, until the correct path is found from the starting position to the answer. Both GPS and the later Soar system are based upon a highly constrained problem solving situation, with an artificial and formally defined problem domain, and with only a rather limited amount of empirical data. It is significant that these authors extrapolated from such a bounded problem solving situation to make an important claim about the nature of general problem solving. Nevertheless GPS is regarded as an important milestone in the history of AI. The goal of general problem solving, where the system itself is context- less, is now seen as overambitious by many AI researchers, yet GPS does not appear to have attracted substantial criticism for involving this extrapolation. Its failure has been seen as more of an implementation problem, and hence necessarily productive